An approach to forecast short-term load of support vector machines based on rough sets

The generalities and specialties of rough sets (RS) and support vector machines (SVM) in knowledge representation and classification are analyzed. A minimum decision network combining RS with SVM in intelligent processing is investigated, and a kind of SVM system on RS is proposed for forecasting. Using RS theory on the advantage of dealing with great data and eliminating redundant information, the system reduced the training data of SVM, and overcame the disadvantage of great data and slow speed. Finally, the system is used to forecast short-term load. The experimental results proved that this approach could achieve greater forecasting accuracy and generalization capability than the BP neural network and standard SVM.